TCATSeg: A Tooth Center-Wise Attention Network for 3D Dental Model Semantic Segmentation
Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Beijing Stomatological Hospital, Capital Medical University（首都医科大学北京 stomatological Hospital）；LargeV Instrument Corporation, Ltd.（LargeV仪器有限公司）
Accurate semantic segmentation of 3D dental models is essential for digital dentistry applications such as orthodontics and dental implants. However, due to complex tooth arrangements and similarities in shape among adjacent teeth, existing methods struggle with accurate segmentation, because they often focus on local geometry while neglecting global contextual information. To address this, we propose TCATSeg, a novel framework that combines local geometric features with global semantic context. We introduce a set of sparse yet physically meaningful superpoints to capture global semantic relationships and enhance segmentation accuracy. Additionally, we present a new dataset of 400 dental models, including pre-orthodontic samples, to evaluate the generalization of our method. Extensive experiments demonstrate that TCATSeg outperforms state-of-the-art approaches.
CUHK-Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Automatic tooth instance segmentation on 3D dental models is a fundamental task for computer-aided orthodontic treatments. Existing learning-based methods rely heavily on expensive point-wise annotations. To alleviate this problem, we are the first to explore a low-cost annotation way for 3D tooth instance segmentation, i.e., labeling all tooth centroids and only a few teeth for each dental model. Regarding the challenge when only weak annotation is provided, we present a dental arch prior-assisted 3D tooth segmentation method, namely DArch. Our DArch consists of two stages, including tooth ce…
Boundary feature fusion network for tooth image segmentation
College of Information Engineering, China Jiliang University（中国计量大学信息工程学院）；College of Optoelectronics, China Jiliang University（中国计量大学光电学院）；College of Computer and Control Engineering, Qiqihar University（齐齐哈尔大学计算机与控制工程学院）
针对牙齿与相邻组织边界模糊问题，本文提出融合边界特征提取与特征交叉融合模块的牙齿分割网络，在STS Data Challenge中获得0.91分，显著优于现有方法。
Automatic teeth segmentation in panoramic x-ray images is an important research subject of the image analysis in dentistry. In this study, we propose a post-processing stage to obtain a segmentation map in which the objects in the image are separated, and apply this technique to tooth instance segmentation with U-Net network. The post-processing consists of grayscale morphological and filtering operations, which are applied to the sigmoid output of the network before binarization. A dice overlap score of 95.4 - 0.3% is obtained in overall teeth segmentation. The proposed post-processing stages…
Innovative Tooth Segmentation Using Hierarchical Features and Bidirectional Sequence Modeling
School of Computer Science and Technology, Zhejiang Gongshang University（浙江工商大学计算机科学与技术学院）；School of Computer and Cyber Sciences, Communication University of China（中国传媒大学计算机与 cyber 科学学院）；Center of Big Data, China Digital Culture Group Co., Ltd（中国数字文化集团大数据中心）；Department of Stomatology, Tongxiang Hospital of Traditional Chinese Medicine（同祥中医医院口腔科）；School of Mathematical and Computational Sciences, Massey University（马希大学数学与计算科学学院）；Shining3D Tech Co., Ltd.（Shining3D科技有限公司）；School of Art Design, Zhejiang Gongshang University（浙江工商大学艺术设计学院）；Zhejiang Key Laboratory of Big Data and Future E-Commerce Technology（浙江大数据与未来电子商务技术重点实验室）
FDNet: Feature Decoupled Segmentation Network for Tooth CBCT Image
Hangzhou Dianzi University（杭州电子科技大学）；College of Media Engineering, Communication University of Zhejiang（浙江传媒学院媒体工程学院）；Shandong University（山东大学）；University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
针对牙齿 CBCT 中伪影复杂、边界模糊的问题，本文提出 FDNet，用 LF-Wavelet 增强全局语义并结合 SAM 编码器细化边界，在基准上取得最优分割性能。
OralBBNet: Spatially Guided Dental Segmentation of Panoramic X-Rays with Bounding Box Priors
Department of Computer Science and Engineering（计算机科学与工程系）；School of IT and Engineering（信息与工程学院）；Indian Institute of Technology Kharagpur（印度理工学院Kharagpur分校）；Kazakh-British Technical University（哈萨克-英国技术大学）；Intelligent Devices Institute（智能设备研究所）；St. Petersburg Electrotechnical University "LETI"（圣彼得堡电力大学"LETI"）；Department of Automation and Control Processes（自动化与控制过程系）
the Ohio State University（俄亥俄州立大学）；Xi’an Jiaotong University（西安交通大学）；United States Air Force（美国空军）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；SOVE Inc（SOVE公司）；ShanghaiTech University（上海科技大学）
Accurately segmenting teeth and identifying the corresponding anatomical landmarks on dental mesh models are essential in computer-aided orthodontic treatment. Manually performing these two tasks is time-consuming, tedious, and, more importantly, highly dependent on orthodontists' experiences due to the abnormality and large-scale variance of patients' teeth. Some machine learning-based methods have been designed and applied in the orthodontic field to automatically segment dental meshes (e.g., intraoral scans). In contrast, the number of studies on tooth landmark localization is still limited…
Seoul National University（首尔国立大学）；Soongsil University（崇实大学）
Individual tooth segmentation and identification from cone-beam computed tomography images are preoperative prerequisites for orthodontic treatments. Instance segmentation methods using convolutional neural networks have demonstrated ground-breaking results on individual tooth segmentation tasks, and are used in various medical imaging applications. While point-based detection networks achieve superior results on dental images, it is still a challenging task to distinguish adjacent teeth because of their similar topologies and proximate nature. In this study, we propose a point-based tooth loc…
Ammonia-Net: A Multi-task Joint Learning Model for Multi-class Segmentation and Classification in Tooth-marked Tongue Diagnosis
School of Mechanical, Electrical & Information Engineering, Shandong University（山东大学机电与信息工程学院）；SDU-ANU Joint Science College, Shandong University（山东大学山东大学-澳大利亚国立大学联合科学学院）；Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院生物医药与健康工程研究院）；School of Software Engineering, Electronic and Information Engineering, Xi'an Jiaotong University（西安交通大学软件与电子信息工程学院）；Department of Radiology, Shenzhen Children's Hospital（深圳市儿童医院放射科）
3D tooth segmentation is an important task for digital orthodontics. Several Deep Learning methods have been proposed for automatic tooth segmentation from 3D dental models or intraoral scans. These methods require annotated 3D intraoral scans. Manually annotating 3D intraoral scans is a laborious task. One approach is to devise self-supervision methods to reduce the manual labeling effort. Compared to other types of point cloud data like scene point cloud or shape point cloud data, 3D tooth point cloud data has a very regular structure and a strong shape prior. We look at how much representat…
Cone-beam computed tomography (CBCT) is a valuable imaging method in dental diagnostics that provides information not available in traditional 2D imaging. However, interpretation of CBCT images is a time-consuming process that requires a physician to work with complicated software. In this work we propose an automated pipeline composed of several deep convolutional neural networks and algorithmic heuristics. Our task is two-fold: a) find locations of each present tooth inside a 3D image volume, and b) detect several common tooth conditions in each tooth. The proposed system achieves 96.3\% acc…
The University of Hong Kong（香港大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；The University of Edinburgh（爱丁堡大学）
In orthodontic treatment, a full tooth model consisting of both the crown and root is indispensable in making the treatment plan. However, acquiring tooth root information to obtain the full tooth model from CBCT images is sometimes restricted due to the massive radiation of CBCT scanning. Thus, reconstructing the full tooth shape from the ready-to-use input, e.g., the partial intra-oral scan and the 2D panoramic image, is an applicable and valuable solution. In this paper, we propose a neural network, called ToothInpaintor, that takes as input a partial 3D dental model and a 2D panoramic imag…
Accurate and automatic segmentation of three-dimensional (3D) individual teeth from cone-beam computerized tomography (CBCT) images is a challenging problem because of the difficulty in separating an individual tooth from adjacent teeth and its surrounding alveolar bone. Thus, this paper proposes a fully automated method of identifying and segmenting 3D individual teeth from dental CBCT images. The proposed method addresses the aforementioned difficulty by developing a deep learning-based hierarchical multi-step model. First, it automatically generates upper and lower jaws panoramic images to …
Seoul National University（首尔国立大学）；Soong-sil University（崇实大学）
Dental panoramic X-ray imaging is a popular diagnostic method owing to its very small dose of radiation. For an automated computer-aided diagnosis system in dental clinics, automatic detection and identification of individual teeth from panoramic X-ray images are critical prerequisites. In this study, we propose a point-wise tooth localization neural network by introducing a spatial distance regularization loss. The proposed network initially performs center point regression for all the anatomical teeth (i.e., 32 points), which automatically identifies each tooth. A novel distance regularizati…
Artificial Intelligence to Assess Dental Findings from Panoramic Radiographs -- A Multinational Study
National Taiwan University（国立台湾大学）；National Taiwan University of Science and Technology（国立台湾科技大学）；Radboud University Medical Center（拉德堡德大学医学中心）；The Ohio State University（俄亥俄州立大学）；National Taiwan University Hospital（国立台湾大学附属医院）
Queen Mary University of London（伦敦玛丽女王大学）；Communication University of Zhejiang（浙江传媒学院）；University of Electronic Science and Technology of China（电子科技大学）；West China Hospital of Stomatology, Sichuan University（四川大学华西口腔医院）；University of Leicester（莱斯特大学）
Accurate tooth volume segmentation is a prerequisite for computer-aided dental analysis. Deep learning-based tooth segmentation methods have achieved satisfying performances but require a large quantity of tooth data with ground truth. The dental data publicly available is limited meaning the existing methods can not be reproduced, evaluated and applied in clinical practice. In this paper, we establish a 3D dental CBCT dataset CTooth+, with 22 fully annotated volumes and 146 unlabeled volumes. We further evaluate several state-of-the-art tooth volume segmentation strategies based on fully-supe…
Accurate 3D Prediction of Missing Teeth in Diverse Patterns for Precise Dental Implant Planning
School of Biomedical Engineering, ShanghaiTech University（上海科技大学生物医学工程学院）；School of Mechanical, Electrical and Information Engineering, Shandong University（山东大学机电与信息工程学院）；School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications（重庆邮电大学通信与信息工程学院）；Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University（上海交通大学医学院附属第九人民医院）；Hangzhou First People’s Hospital, Zhejiang University（浙江大学附属杭州市第一人民医院）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）
ArchMap: Arch-Flattening and Knowledge-Guided Vision Language Model for Tooth Counting and Structured Dental Understanding
Xi'an Jiaotong-Liverpool University（西交利物浦大学）；University of Liverpool（利物浦大学）；Zhejiang University of Technology（浙江工业大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
A Multi-Stage Framework for 3D Individual Tooth Segmentation in Dental CBCT
School of Artificial Intelligence, Guilin University of Electronic Technology（桂林电子科技大学人工智能学院）；Guangxi Colleges and Universities Key Laboratory of AI Algorithm Engineering（广西高校人工智能算法工程重点实验室）
Fully automatic integration of dental CBCT images and full-arch intraoral impressions with stitching error correction via individual tooth segmentation and identification
Yonsei University（延世大学）；College of Dentistry, Yonsei University（延世大学牙科学院）；Oral Science Research Center（口腔科学研究中心）
We develop a Computer Aided Diagnosis (CAD) system, which enhances the performance of dentists in detecting wide range of dental caries. The CAD System achieves this by acting as a second opinion for the dentists with way higher sensitivity on the task of detecting cavities than the dentists themselves. We develop annotated dataset of more than 3000 bitewing radiographs and utilize it for developing a system for automated diagnosis of dental caries. Our system consists of a deep fully convolutional neural network (FCNN) consisting 100+ layers, which is trained to mark caries on bitewing radiog…
Memory-Guided Point Cloud Completion for Dental Reconstruction
College of Information and Intelligent Science, Donghua University, Shanghai, China（信息与智能科学学院，东华大学，上海，中国）；Institute of Artificial Intelligence, Shanghai University, Shanghai, China（人工智能研究院，上海大学，上海，中国）
PanDent: Toward Comprehensive Tooth-Level Structure-Language Consistency in Dental Radiology
Faculty of Dentistry, The University of Hong Kong（香港大学牙医学院）；Imperial College London（帝国理工学院）；University of Science and Technology of China（中国科学技术大学）；Department of Data and Systems Engineering, The University of Hong Kong（香港大学数据与系统工程系）；Department of Electronic Engineering, The Chinese University of Hong Kong（香港中文大学电子工程系）
Adapting Foundation Model for Dental Caries Detection with Dual-View Co-Training
School of Biomedical Engineering \& State Key Laboratory of Advanced Medical Materials；Devices, ShanghaiTech University, Shanghai, China Lingang Laboratory, Shanghai, China Shanghai United Imaging Intelligence Co. Ltd., Shanghai, China Shanghai Clinical Research；Trial Center, Shanghai, China Shanghai Linkedcare Information Technology Co., Ltd., Shanghai, China
National Institutes of Health（美国国立卫生研究院）；Purdue University（普渡大学）；Hill’s Pet Nutrition（希尔斯宠物营养公司）
Biofilm is a formation of microbial material on tooth substrata. Several methods to quantify dental biofilm coverage have recently been reported in the literature, but at best they provide a semi-automated approach to quantification with significant input from a human grader that comes with the graders bias of what are foreground, background, biofilm, and tooth. Additionally, human assessment indices limit the resolution of the quantification scale; most commercial scales use five levels of quantification for biofilm coverage (0%, 25%, 50%, 75%, and 100%). On the other hand, current state-of-t…
École Polytechnique de Montréal（蒙特利尔理工学院）；Centre d'intelligence artificielle appliquée (JACOBB)（应用人工智能中心（JACOBB））；Intellident Dentaire Inc.（Intellident牙科公司）
Teeth segmentation is an important topic in dental restorations that is essential for crown generation, diagnosis, and treatment planning. In the dental field, the variability of input data is high and there are no publicly available 3D dental arch datasets. Although there has been improvement in the field provided by recent deep learning architectures on 3D data, there still exists some problems such as properly identifying missing teeth in an arch. We propose to use spectral clustering as a self-supervisory signal to joint-train neural networks for segmentation of 3D arches. Our approach is …
Caries DETR: Tooth Structure-aware Prior and Lesion-aware Dynamic Loss Refinement for DETR Based Caries Detection
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）；Department of Stomatology, Shenzhen University General Hospital（深圳大学附属医院口腔科）
National Institutes of Health（美国国立卫生研究院）；Purdue University（普渡大学）；Hill’s Pet Nutrition（希尔斯宠物营养公司）
Dental biofilm is the deposition of microbial material over a tooth substratum. Several methods have recently been reported in the literature for biofilm quantification; however, at best they provide a barely automated solution requiring significant input needed from the human expert. On the contrary, state-of-the-art automatic biofilm methods fail to make their way into clinical practice because of the lack of effective mechanism to incorporate human input to handle praxis or misclassified regions. Manual delineation, the current gold standard, is time consuming and subject to expert bias. In…
Designing a synthetic crown is a time-consuming, inconsistent, and labor-intensive process. In this work, we present a fully automatic method that not only learns human design dental crowns, but also improves the consistency, functionality, and esthetic of the crowns. Following success in point cloud completion using the transformer-based network, we tackle the problem of the crown generation as a point-cloud completion around a prepared tooth. To this end, we use a geometry-aware transformer to generate dental crowns. Our main contribution is to add a margin line information to the network, a…
Zhejiang University（浙江大学）；Angelalign Research Institute（时代天使研究院）；Angel Align Inc.（时代天使公司）；A*STAR Centre for Frontier AI Research (CFAR)（新加坡科技研究局前沿人工智能研究中心）；Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine（浙江大学医学院附属口腔医院）；Harvard University（哈佛大学）
Optical Intra-oral Scanners (IOS) are widely used in digital dentistry, providing 3-Dimensional (3D) and high-resolution geometrical information of dental crowns and the gingiva. Accurate 3D tooth segmentation, which aims to precisely delineate the tooth and gingiva instances in IOS, plays a critical role in a variety of dental applications. However, segmentation performance of previous methods are error-prone in complicated tooth-tooth or tooth-gingiva boundaries, and usually exhibit unsatisfactory results across various patients, yet the clinically applicability is not verified with large-sc…
DENTEX: Dental Enumeration and Tooth Pathosis Detection Benchmark for Panoramic X-ray
Department of Quantitative Biomedicine, University of Zurich（苏黎世大学定量生物医学系）；Harvard Medical School（哈佛医学院）；Department of Computer Science, ETH Zurich（苏黎世联邦理工学院计算机科学系）；ETH AI Center, ETH Zurich（苏黎世联邦理工学院人工智能中心）；Center for Dental Medicine, University of Zurich（苏黎世大学牙科医学中心）；Department of Computer Engineering, Bogazici University（博雅大学计算机工程系）；Department of Endodontics, Istanbul Medipol University（伊斯坦布尔Medipol大学牙体医学系）；University of Oklahoma, College of Dentistry, Graduate Periodontics Department（俄克拉荷马大学牙科学院研究生牙周病学系）
Generating Findings for Jaw Cysts in Dental Panoramic Radiographs Using a GPT-Based VLM: A Preliminary Study on Building a Two-Stage Self-Correction Loop with Structured Output (SLSO) Framework
Gifu University（岐阜大学）；EyeTech Co., Ltd（EyeTech有限公司）；Asahi University（朝日大学）；Shiga University（滋贺大学）；The University of Osaka Dental Hospital（大阪大学齿科医院）
Image processing techniques has been widely used in dental researches such as human identification and forensic dentistry, teeth numbering, dental carries detection and periodontal disease analysis. One of the most challenging parts in dental imaging is teeth segmentation and how to separate them from each other. In this paper, an automated method for teeth segmentation of Periapical dental x-ray images which contain at least one root-canalled tooth is proposed. The result of this approach can be used as an initial step in bone lesion detection. The proposed algorithm is made of two stages. Th…
Sparse Anatomical Prompt Semi-Supervised Learning with Masked Image Modeling for CBCT Tooth Segmentation
Institute of Innovative Research, Tokyo Institute of Technology（东京工业大学创新研究院）；Stomatological Hospital of Chongqing Medical University（重庆医科大学附属口腔医院）；School of Mechanical Engineering, Zhejiang University（浙江大学机械工程学院）
TeethGenerator: A two-stage framework for paired pre- and post-orthodontic 3D dental data generation
Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Beijing Stomatological Hospital, Capital Medical University（首都医科大学北京 stomatological Hospital）
STS MICCAI 2023 Challenge: Grand challenge on 2D and 3D semi-supervised tooth segmentation
Communication University of Zhejiang（浙江传媒学院）；West China Hospital of Stomatology, Sichuan University（四川大学华西口腔医院）；Lishui University（丽水学院）；Hangzhou Geriatric Stomatology Hospital（杭州老年口腔医院）；Hangzhou Dental Hospital Group（杭州市口腔医院集团）；Hangzhou Dianzi University（杭州电子科技大学）；Suzhou Research Institute of Shandong University（山东大学苏州研究院）；Shanghai Jiao Tong University（上海交通大学）；Shenzhen University（深圳大学）；Queen Mary University of London（伦敦大学玛丽皇后学院）；Shandong University（山东大学）；Sichuan Provincial People’s Hospital（四川省人民医院）；University of Electronic Science and Technology of China（电子科技大学）
本文介绍了 STS MICCAI 2023 挑战赛，旨在利用半监督算法解决 2D 全景 X 射线和 3D CBCT 牙齿分割问题，并总结了排名靠前队伍的方法，以推动牙科自动分析发展。
ImplantFormer: Vision Transformer based Implant Position Regression Using Dental CBCT Data
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；AI Research Center for Medical Image Analysis and Diagnosis, Shenzhen University（深圳大学医学影像分析与诊断AI研究中心）；National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University（深圳大学大数据系统计算技术国家工程实验室）；Department of Stomatology, Shenzhen University General Hospital（深圳大学总医院口腔科）；the School of Dentistry, Shenzhen University（深圳大学口腔医学院）
